BRIEFING NOTE

by Charlotte Duval-Lantoine

What does it mean to conduct Gender-Based Analysis Plus (GBA+) on a military’s integration of emerging technologies? The way these technologies are developed and used, even in a context of great power competition and deterrence, can reveal and perpetuate an organization’s biases. This briefing note seeks to deconstruct where the Department of National Defence (DND) and Canadian Armed Forces (CAF) can leverage GBA+ not only to mitigate unconscious biases and potential harm, but also to make its integration of emerging technologies more effective. 

Context 

• Gender-Based Analysis Plus is a tool the Government of Canada, including the Department of National Defence, uses to understand how policies and programs affect different groups of people and to “attain better results for Canadians by being more responsive to specific needs and circumstances.” 
  • Further institutionalization of GBA+ is one of DND’s goals for 2024-25. 
  • DND’s Chief Information Officer Group is lacking the data “to monitor and/or report program impacts by gender and diversity,” but “continues to look for entry points for GBA Plus data collection, monitoring and evaluation.”  
• DND/ CAF are looking to integrate emerging technologies to enhance their operational effects, situational awareness, deterrence capabilities, and enterprise activities. 
  • They are keenly aware that a substantial amount of work needs to be done at the corporate level for the proper integration of the new and emerging technologies. 
  • However, none of the multiple strategies and campaign plans to help the CAF achieve this integration contains a GBA+ assessment. 

Biases in Technologies: The Example of Artificial Intelligence and Machine Learning 

• Emerging digital technologies, including AI, may reconstitute societal biases because of the involvement of humans in their development. How humans develop and use emerging technology can reflect profound and preexisting assumptions that can harm historically marginalized groups. 
• In a security and defence context, assumptions can lead to an incomplete operational picture; a tragic example is evidence that gender biases contributed to Israeli intelligence failure ahead of the October 7, 2023 attacks by Hamas..
• In its recently released AI Strategy, the CAF recognizes that algorithm-based technologies such as artificial intelligence (AI) and machine learning (ML) can contain inherent biases that can create and perpetuate harms towards a given population. However, the document does not discuss the challenge further. 
  • AI bias can develop due to biases in the data used to train it and programming errors. All are due to how humans training the AI weigh, select, and label data. 
  • An extreme example of this dates to 2016, when Microsoft introduced an AI robot to the social media platform Twitter (now X). Within 24 hours, the robot started posting antisemitic tweets. A March 2024 analysis shows that the issue of AI/ ML technologies absorbing racial prejudice has yet to be resolved. 
• Additional bias can include automation bias, described as an increasing tendency to favour autonomous solutions to various challenges. In defence, this may encourage the use of autonomous systems, including unmanned platforms, when doing so is not necessary, which can undermine operational effectiveness. 
• It is important for the CAF to prepare for these types of biases and find means to mitigate their effect. But even before it does so, the CAF needs to be diligent in how it integrates new technologies. 

Integrating Technologies: A Bias Minefield?

• A conversation often held within defence circles is the use of technologies (AI/ ML, sensors, automation) to liberate personnel’s time for more complex tasks, a critical endeavour in times of personnel shortages and CAF Reconstitution.
• To successfully integrate these technologies, the CAF will have to review its personnel management and generations processes, by occupation and position.
  • This review would serve to identify the type of skills needed to ensure a smooth integration of technologies; to grapple with how these technologies are impacting the division of labour within the CAF; and to anticipate how technologies will change the organization’s way of working.
  • Shorter-term, the review would need to also examine how to develop the career of “actors of change” and prioritize their accession to leadership positions. 
• A rigorous GBA+ that considers how diverse groups are distributed within the CAF (i.e., what ranks and occupations they occupy the most), and how emerging technology can affect that distribution, may be necessary. 
  • g., most women in the CAF are in an administrative role. What impact would the integration of AI-assisted virtual assistants have on this particular workforce? 
  • It is understood that integration of advanced technologies will require favouring more STEM-oriented knowledge bases and skill sets. Less than 25 percent of the civilian STEM workforce are women.  
  • Opportunities for greater diversity should also be examined: emerging technologies can allow for the military to expand its recruiting pool and may allow individuals with medical conditions and disability to join the ranks. An individual remotely operating a vehicle does not require the same level of fitness as a combat engineer, a pilot, or a naval warfare officer.
  • g., a cyber operator whose role is to produce operational effects from a base or a headquarter does not need to meet the same physical standards as a service member in a combat role – even though universality of service requires it by policy. 
  • The U.S. Space Force is a good example of a service studying how to better adapt health and physical standards to the occupation and tasks of their troops.
• In a similar vein, the CAF will have to reflect on how the integration of emerging technology in non-operational settings will impact the non-commissioned corps: 
  • AI adoption in the workforce will impact jobs that are considered lower skilled (which is usually understood as a role that does not require a bachelor’s degree). 
  • There is a question as to how this might impact what is understood as the CAF’s main recruitment base, i.e., rural, poorer Canadians with no post-secondary education. 

Conclusion

Examining the GBA+ implications of the CAF’s digital and technological transformation is critical for its success. A thorough GBA+ could help identify the reforms necessary and how they will impact CAF personnel, including diverse groups. It would help ensure that the CAF’s technology era does not impede on cultural evolution and the efforts of recruiting and retaining employment equity-seeking and 2SLGBTQIA+ groups, while ensuring operational effectiveness. The adoption and integration of emerging technology require rigorous and deliberate planning that would allow the CAF to develop a culture that is best adapted to new technologies and their continuous advancement, but also one that lets its personnel thrive and serve in meaningful ways.